Electromyography (EMG)
19 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Electromyography (EMG)
Silent Speech EMG
Subtasks
Most implemented papers
Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer
larocs/EMG-GAN
•
•
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals.
sEMG Gesture Recognition with a Simple Model of Attention
Myoelectric control is one of the leading areas of research in the field of robotic prosthetics.
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals
The link between different psychophysiological measures during emotion episodes is not well understood.
EV-Action: Electromyography-Vision Multi-Modal Action Dataset
To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.
Parkinson’s Disease EMG Signal Prediction Using Neural Networks
This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
coreylammie/TBCAS-Towards-Healthcare-and-Biomedical-Applications
•
•
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.
Digital Voicing of Silent Speech
dgaddy/silent_speech
•
•
EMNLP 2020
In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses.
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues.
An Improved Model for Voicing Silent Speech
dgaddy/silent_speech
•
•
ACL 2021
In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals.
Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation
A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks.
